Multimodal Facility Surveillance for Reliable Critical State Detection
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Solution Overview
Problem
Automated surveillance systems relying solely on RGB- or IR-picture information for object or person detection are not robust, especially under challenging conditions such as night, unusual illumination, or dissimulation, and existing fusion approaches face limitations in accuracy and reliability.
Innovation Solution
A facility surveillance system that combines data from multiple sensors, including RGB cameras, depth cameras, infrared cameras, microphones, and thermal sensors, using a central computing unit to generate a dynamic building information model and classify state patterns as critical or non-critical, employing machine learning and multimodal fusion techniques to enhance detection accuracy and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors are combined for surveillance, then detection accuracy and reliability are improved, but device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (RGB cameras, depth cameras, infrared cameras, microphones, thermal sensors) into a unified surveillance system that integrates their data through a central computing unit. This merging of sensors allows the system to overcome the limitations of individual sensors and achieve more reliable detection under challenging conditions.
Solution Approach 2:
The central computing unit serves multiple functions: it receives data from various sensor types, processes the data using machine learning algorithms, generates building information models, and performs classification of state patterns. This multi-functional approach consolidates complexity into a single processing unit rather than requiring separate systems for each function.
2Measurement precision
If machine learning and multimodal fusion techniques are employed, then detection accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary processing of sensor data by generating building information models and pre-processing surveillance data before classification. This preliminary action organizes the data in advance, making the subsequent machine learning classification more efficient and reducing the computational burden during real-time operation.
Solution Approach 2:
The system dynamically adjusts its processing based on the situation by classifying state patterns as critical or non-critical. This dynamic classification allows the system to focus computational resources on critical anomalies that require immediate attention, rather than processing all detected states with equal computational effort.
3Reliability
If criticality classification is performed, then false positives are reduced, but system complexity increases
Solution Approach 1:
The classification process is segmented into distinct stages: first, surveillance data is processed to detect state patterns; second, the criticality of these patterns is classified; and third, alerts are generated only for critical patterns. This segmentation allows the system to reduce false positives by filtering non-critical patterns without requiring a single overly complex classification model.
Solution Approach 2:
The system changes the parameter of classification by introducing criticality assessment as an additional dimension. Instead of simply detecting anomalies, the system evaluates whether detected anomalies are critical or non-critical, using this parameter change to reduce false positives while managing complexity through structured classification categories.
Data Source
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Figure 2b~2c
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AI summary
The invention pertains to systems and methods for surveillance of a facility.